In recent years, networks found by Neural Architecture Search (NAS) methods are surpassing human-designed ones, setting state-of-the-art performance on various tasks [1]}, [2]}. Ultimately, NAS calls for algorithmic solutions that can discover near-optimal models for any task within any search space under moderate computational budgets. To pursue this goal, recent research in NAS quickly expanded its scope into broader vision domains such as object detection [3]} and semantic segmentation [4]}. [5]} seeks to bridge the gap between sample-based and one-shot approaches. Besides searching for an optimal cell design in earlier works [6]}, many recent works also investigate the macro skeleton search of a network [7]}, [3]}.
